Summary of Deterministic Exploration Via Stationary Bellman Error Maximization, by Sebastian Griesbach et al.
Deterministic Exploration via Stationary Bellman Error Maximization
by Sebastian Griesbach, Carlo D’Eramo
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed modifications to the Bellman error as a separate optimization objective for exploration in reinforcement learning (RL) aim to stabilize deterministic exploration policies. The method introduces three components: accounting for previous experiences, episode-length agnosticism, and far-off-policy learning mitigation. Experimental results demonstrate that this approach can outperform -greedy in both dense and sparse reward settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a way for machines to learn by trying new things and getting rewards or penalties. The problem is that it’s hard for the machine to know when to try something new. Researchers have tried different methods to help, such as adding noise or giving rewards for exploring. This paper introduces three new ideas to make this process more stable and effective. The goal is to create a system that can decide when to explore and learn from its experiences. The results show that this approach works better than another popular method in some situations. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
